Project Information
- Partner: Geekulcha (on behalf of DEDAT NC)
- Students: Kopano Motlapele (SPU), Lebohang Molapo (SMU), Boitumelo Matlapeng (UCT)
- Project Lead: Prof Sonali Das
- Project Mentors: Mapitsi Rangata
- Year: 2017/2018
Project Description
Northern Cape is the largest province in South Africa that is known to have the lowest population estimate compared to the other provinces. It boasts a colourful history with a variety of cultural tourist attractions and is particularly well known for its many desert-like areas. Mining has always defined the history in this part of South Africa, and when diamonds were discovered in Kimberley unprecedented growth took place in the province. Weather has an impact on Tourism, it has been observed over the years to influence the decision making of the tourists. It affects the frequency of tourism, the selection of the destination, tourists activities and the vacation satisfaction. Some tourists attraction sites are weather dependent, e.g the Namaqualand which is filled with seasonal flowers. So weather can be considered a determinant of the success of tourism in a location, controlling the tourist flow. It is important to monitor weather, since it controls the flow of tourists to a place and project dedat aims to increase or better the tourism flow in Northern Cape. In this project, a case study is made to understanding the flow of tourists in NC in comparison to the weather. Two datasets with different frequencies were used, namely tourism in NC and the weather data. In the tourism data, we had two categories namely foreign and domestic ranging from (2013 -2016) and (2009-2016) respectively, with common variables: the “average length of stay”, “visits”, “bednights”, “purpose of visits” and “cities”. The weather data ranges from (2012 – 2016) and temperature and humidity were the only considered variables. The aim was to build a regression model to predict and advice the DEDAT in NC on how the weather affects the tourist and how to mitigate this effect, but due to the variation of the frequency (tourism is observed annually and weather hourly) in the dataset it was difficult to come up with a decisive tool.
Northern Cape located on the map of South Africa with two cities in which most tourist activity take place.
Team
